We performed a comparison between Apache Flink and Google Cloud Dataflow based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."
"It is user-friendly and the reporting is good."
"The setup was not too difficult."
"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."
"This is truly a real-time solution."
"With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts."
"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."
"Apache Flink's best feature is its data streaming tool."
"The best feature of Google Cloud Dataflow is its practical connectedness."
"The support team is good and it's easy to use."
"The most valuable features of Google Cloud Dataflow are scalability and connectivity."
"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."
"It is a scalable solution."
"Google Cloud Dataflow is useful for streaming and data pipelines."
"The service is relatively cheap compared to other batch-processing engines."
"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."
"The state maintains checkpoints and they use RocksDB or S3. They are good but sometimes the performance is affected when you use RocksDB for checkpointing."
"There is room for improvement in the initial setup process."
"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."
"The machine learning library is not very flexible."
"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"The solution could be more user-friendly."
"Apache Flink's documentation should be available in more languages."
"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."
"The authentication part of the product is an area of concern where improvements are required."
"The technical support has slight room for improvement."
"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."
"The solution's setup process could be more accessible."
"They should do a market survey and then make improvements."
"The deployment time could also be reduced."
"There are certain challenges regarding the Google Cloud Composer which can be improved."
Apache Flink is ranked 5th in Streaming Analytics with 15 reviews while Google Cloud Dataflow is ranked 7th in Streaming Analytics with 10 reviews. Apache Flink is rated 7.6, while Google Cloud Dataflow is rated 7.8. The top reviewer of Apache Flink writes "A great solution with an intricate system and allows for batch data processing". On the other hand, the top reviewer of Google Cloud Dataflow writes "Easy to use for programmers, user-friendly, and scalable". Apache Flink is most compared with Spring Cloud Data Flow, Amazon Kinesis, Databricks, Azure Stream Analytics and Informatica Data Engineering Streaming, whereas Google Cloud Dataflow is most compared with Databricks, Apache NiFi, Amazon MSK, Amazon Kinesis and Informatica Data Engineering Streaming. See our Apache Flink vs. Google Cloud Dataflow report.
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